Abstract
Advanced energy management strategies (EMS) are used to control the power flow through a vehicle’s powertrain. However, the cost of high-power computational hardware and lack of a priori knowledge of future road conditions poses difficult challenges for engineers attempting to implement globally optimal frameworks. One solution is to use advanced driver assistance systems (ADAS) and connectivity to obtain a prediction of future road conditions. This paper presents a look-ahead predictive EMS which combines approximate dynamic programming (ADP) methods and an adaptive equivalent consumption minimization strategy (A-ECMS) to obtain a near-optimal solution for a future prediction horizon. ECMS is highly sensitive to the equivalence factor (EF), making it necessary to adapt during a trip to account for disturbances. A novel adaptation method is presented in this work which uses an artificial neural network to learn the nonlinear relationship between a speed and the state of charge (SOC) trajectory prediction obtained from ADP to estimate the corresponding EF. A traffic uncertainty analysis demonstrates an approximately 10% fuel economy (FE) improvement over traditional A-ECMS. Using a data-driven adaptation method for A-ECMS informed by a dynamic programming (DP) based prediction results in an EMS capable of online implementation.
Highlights
Government regulations worldwide are pushing toward clean, renewable energy to reduce pollution levels and create a sustainable future
This paper presented a novel approach for integrated energy management strategies (EMS) and speed control using adaptive equivalent consumption minimization strategy (A-equivalent consumption minimization strategy (ECMS)) considering look-ahead data
A unique combination of a dynamic programming (DP)-based optimal control solution for speed profiling and ECMS with an optimal equivalence factor (EF) predictor was shown to be capable of online implementation on vehicle hardware
Summary
Government regulations worldwide are pushing toward clean, renewable energy to reduce pollution levels and create a sustainable future. The automotive industry has invested heavily in innovative technology such as electrified powertrains. Electrification of powertrains provides attractive benefits over conventional powertrains, including engine downsizing, electric motor assist, and engine start-stop. The automation of driving promises safer driving; the automotive industry is embracing connected and automated vehicle (CAV) technology. A CAV uses advanced on-board sensing technology to obtain information about surroundings (commonly referred to as look-ahead data) and make informed decisions about present and future actions. Perception and control algorithms, such as sensor fusion and adaptive cruise control (ACC), leverage this information to make vehicles safer and more efficient
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